System and method for identifying vaping and bullying

ABSTRACT

A sensor system for identifying vaping, other smoking activities, and bullying at a site includes an air quality sensor configured to detect air quality, a sound detector configured to detect sounds, and a network interface configured to transmit a signal indicating abnormality matching signature of vaping, other smoking activity, or sound of bullying. Vaping or another smoking activity is identified based on the detected air quality, and bullying is identified based on the detected sound.

CROSS REFERENCE TO RELATED APPLICATION

The present application is a continuation of International ApplicationNo. PCT/US2018/000223, filed on Aug. 15, 2018, which claims the benefitof and priority to U.S. Provisional Application No. 62/545,795, filed onAug. 15, 2017. The entire contents of each of the foregoing applicationsare hereby incorporated by reference herein.

BACKGROUND Technical Field

The present disclosure relates to a system and method for identifyingvaping and bullying at an enclosed site. More particularly, the presentdisclosure relates to a sensor system which includes an air qualitysensor for detecting air quality and a sound sensor for detectingsounds.

Background of Related Art

Vaping and bullying have been serious problems in enclosed areas ofacademic/business environments due to hazardous/harmful impacts onpeople. Various methods and systems have been developed to identify orprevent bullying and vaping in enclosed areas, such as classrooms,restrooms, bathrooms, storage rooms, hospital rooms, or other kinds ofenclosed areas in a school, hospital, warehouse, cafeteria, offices,financial institutes, governmental buildings, or any business entities.For example, bullying and vaping/smoking can be identified by camerasurveillance. However, such camera surveillance systems have not beenused in private areas such as restrooms, bathrooms, shower rooms, orhospital rooms because privacy has more weights than identification ofbullying and vaping/smoking.

Bullying can be detected by a sound sensor at the site. However, thereare many other sounds (e.g., flushing, conversions, cleaning, gaming, orsounds from outside) preventing from identification of bullying. Thus,further developments are needed in identification of bullying atenclosed sites.

Vaping becomes more popular in young aged people and causes many healthand environmental issues. Generally, vaping has similar effects onpeople around in close proximity of the smokers. Thus, by identifyingvaping or other smoking activities in enclosed areas, people can besupervised appropriately so that harmful and hazardous effects can beprevented. Accordingly, effective identification of vaping/smoking andbullying is in dire need in academic/business environments for safetyand public health purposes.

SUMMARY

The present disclosure features a sensor system and an identificationsystem, which includes an air quality sensor and a sound sensor foridentifying vaping and bullying.

In an embodiment, a sensor system for identifying vaping, other smokingactivities, and bullying at a site includes an air quality sensorconfigured to detect air quality, a sound detector configured to detectsounds, and a network interface configured to transmit a signalindicating abnormality matching signature of vaping, other smokingactivity, or sound of bullying. The vaping or another smoking activityis identified based on the detected air quality, and the bullying isidentified based on the detected sound.

In an aspect, the sensor system is powered via power over Ethernet orpower over Ethernet+. The power is supplied by a CAT5, CAT5E, or CAT6cable. The detected air quality and the detected sounds are transmittedwirelessly or via the CAT5, CAT5E, or CAT6 cable.

In another aspect, the vaping or the another smoking activity isidentified when the detected air quality includes a signature. Thesignature includes a temperature range, a hydrogen range, and a humidityrange.

In yet another aspect, the air quality sensor is location-independent.

In yet another aspect, the sound detector is location-dependent. Thesound detector is to detect sounds in a predetermined period in alearning mode prior to identification of the bullying. The detectedsounds during the predetermined period generate base data foridentifying the bullying at the site. The bullying is identified whenthe detected sounds are greater than or equal to a threshold value basedon the environment calibrated data.

In yet another aspect, an alert is transmitted silently from the site toa user when the vaping, or another smoking activity, or bullying isdetected. The alert is a text message, an email, an optical flashing, anaudible sound, or combination thereof.

In still yet another aspect, the sensor system is run by a mobileoperating system.

In another embodiment, an identification system for identifying vaping,other smoking activities, and bullying includes a sensor system disposedat a site and a controller coupled to the sensor system via a network.The sensor system includes an air quality sensor configured to detectair quality, a sound detector configured to detect sounds, and a networkinterface configured to transmit a signal indicating abnormalitymatching signature of vaping, other smoking activity, or sound ofbullying. The controller is configured to identify vaping or anothersmoking activity based on the sensed air quality, to identify bullyingbased on the detected sounds, and to send an alert to a user.

In an aspect, the sensor system is powered via power over Ethernet orpower over Ethernet+. The power is supplied by a CAT5, CAT5E, or CAT6cable. The detected air quality and the detected sounds are transmittedwirelessly or via the CAT5, CAT5E, or CAT6 cable.

In another aspect, the vaping or the another smoking activity isidentified when the detected air quality includes a signature. Thesignature includes a temperature range, a hydrogen range, and a humidityrange.

In yet another aspect, the air quality sensor is location-independent.

In yet another aspect, the sound detector is location-dependent. Thesound detector is to detect sounds in a predetermined period in alearning mode prior to identification of the bullying. The detectedsounds during the predetermined period generate base data foridentifying the bullying at the site. The bullying is identified whenthe detected sounds are greater than or equal to a threshold value basedon the environment calibrated data.

In yet another aspect, an alert is transmitted silently from the site toa user when the vaping, or another smoking activity, or bullying isdetected. The alert is a text message, an email, an optical flashing, anaudible sound, or combination thereof.

In still yet another aspect, the sensor system is run by a mobileoperating system.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the features and advantages of the disclosedtechnology will be obtained by reference to the following detaileddescription that sets forth illustrative embodiments, in which theprinciples of the technology are utilized, and the accompanying drawingsof which:

FIG. 1 is a block diagram of an identification system for identifyingbullying and vaping/smoking in accordance with embodiments of thepresent disclosure;

FIG. 2 is a functional block diagram of the detection sensor of FIG. 1in accordance with embodiments of the present disclosure;

FIG. 3A is a graphical illustration showing detected sound results fromthe detection sensor of FIG. 1 in accordance with embodiments of thepresent disclosure;

FIGS. 3B and 3C are graphical illustration showing history data from thedetection sensor of FIG. 1 in accordance with embodiments of the presentdisclosure;

FIG. 4 is a flowchart showing a learning mode for the detection sensorin accordance with embodiments of the present disclosure;

FIG. 5 is a flowchart showing an active mode for the detection sensor inaccordance with embodiments of the present disclosure;

FIG. 6 is a flowchart showing a method for detecting vaping inaccordance with embodiments of the present disclosure; and

FIG. 7 is a functional block diagram of a computing device in accordancewith embodiments of the present disclosure.

DETAILED DESCRIPTION

This disclosure relates to identification systems and detection sensorsfor detecting air quality and sound to identify whether bullying andvaping (or other smoking activities) occurs at enclosed sites. Whenbullying and/or vaping are identified, warnings or alerts aretransmitted to registered users or clients without providing anyindication of warnings to one or more persons who vape or bully at thesite. In this way, one or more persons who bully or vape can be properlyreported and appropriately supervised later. Further, one or morepersons near the vaping or bullying can be effectively prevented fromfurther harms.

FIG. 1 illustrates a block diagram showing an identification system 100according to embodiments of the present disclosure. The identificationsystem 100 includes a plurality of detection sensors 110, which detectair quality related to vaping and sound related to noise disturbance atenclosed sites. The identification system 100 further includes a controlserver 120 for identifying whether or not vaping or bullying occurs atthe enclosed site, and a database 130 storing base data for identifyingbullying and history data of detected sounds and air quality at eachenclosed site.

The detected air quality may be analyzed by the detection sensors 110 orthe detected air quality may be transmitted to the control server 120together with the detected sound. The control server 120 may analyze thedetected sound based on base data stored at the database 130 and thedetected air quality, and determine whether bullying and/or vapingoccurs at the enclosed sites. The base data stored at the database 130may be location-dependent, meaning that the base data for one locationis different from that for another site. The location-dependent basedata may be sound data related to identifying bullying. For example, ata bathroom, there are flushing sounds, conversions, cleaning sounds, andetc. Based on the size of the bathroom and the installation location ofthe detection sensor 110, the detection sensor 110 may detect soundsdifferently from other detection sensors 110 installed at the bathroomor at a bedroom near the bathroom. Thus, the location-dependent basedata may be different based on the installation locations at the samesite.

For these reasons, the location-dependent base data is to be obtained atthe site for a period in a learning mode. The period may vary dependingon the installation location, the time, the day of the week, and thedate. The location-dependent base data may be obtained for a period,which is determined based on the environment of the enclosed site andthe installation location of the detection sensor 110. After obtaininglocation-dependent base data for a period sufficiently long enough toform profile for the location, the detection sensor 110 may be turnedinto an active mode to identify noise disturbance.

In an aspect, when the detection sensor 110 transmits detected resultsto the control server 120, the control server 120 may acquire from thedatabase 130 the profile for the location where the detection sensor 110is installed and the time when the detected results is obtained, andanalyzes the detected results to identify occurrence of bullying basedon the base data.

In an aspect, the detected sounds may be used to identify sleep apnea.Sleep apnea is a serious sleep disorder that occurs when a person'sbreathing is interrupted while sleeping. People with untreated sleepapnea stop breathing repeatedly during their sleep. This means the brainand the rest of the body may not get enough oxygen. Sleep apnea can leadto more serious problems such as high blood pressure, stroke, heartfailure, and diabetes.

Similar to bullying, base data for sleep apnea may be obtained duringthe learning mode prior to identifying sleep apnea. During the learningmode, the detection sensor 110 may record decibel levels of the sleepingsounds of a person over a temporal period, which may be more or lessthan one week. The base data may contain patterns of the person'sbreathing at times when the lulls in breathing and loud spikes occur.

In another aspect, the detection sensor 110 may save the base data in amemory (which is not shown) of the detection sensor 110. In other words,the detection sensor 110 may determine vaping, bullying, or sleep apneaby itself at the site where the detection sensor 110 is installed. Inthis case, the detection sensor 110 transmits signals indicatingabnormality matching signature of vaping, bully, or apnea. This ensuresdata privacy, meaning that the data stay within the detection sensor110, and further ensures privacy of people at the site.

During the active mode, the detection sensor 110 may listen to theperson's sleeping sounds and the control server 120 may compare thecurrent levels (e.g. decibels) of the sleeping with the expected levelfrom the base data at the corresponding time. The comparing data may bedisplayed so that the user can see when sleep apnea occurs. The controlserver 120 may measure anomalies in sound over a predicted norm. Thecontrol server 120 may determine patterns of snoring, breathing, or anysound disruption during the sleep by analyzing the sound amplitudepattern that occurs. By analyzing the amplitude of the sound as well asirregular levels of sound in the sleep pattern, the control server 120may identify sleep apnea.

In an aspect, the base data may be location-independent, meaning thatthe base data is the same for every enclosed location at every time. Thelocation-independent base data may be air quality data related toidentifying vaping. Since vaping has a signature in temperature,humidity, and hydrogen ranges, vaping may be identified based on thesignature. In an aspect, features for identifying vaping may beintegrated into the detection sensor 110 so that the detection sensor110 may request an alert or warning message to be sent to the clients170, when the signature is identified in the detected air quality. Thesignature may include combination of predetermined ranges oftemperature, humidity, and hydrogen.

Generally, hydrogen sensors require at least 7 volts and about 1,000 ohmresistance. The detection sensor 110, however, may have a modifiedhydrogen sensor, which requires much lower voltage and a much higherresistance. The voltage and resistance may vary based on temperature ofthe environment.

The database 130 may further include history data which is time-seriesand location-specific data for identifying bullying for each locationwhere the detection sensor 110 has been installed. In an aspect, thecontrol server 120 may analyze the history data to predict occurrencesof vaping and bullying at the location so that appropriate actions maybe proactively and precautiously taken at the location.

In an aspect, the control server 120 may analyze the history data storedat the database 130 to identify trend of the history data. The trend maybe a decrease or increase pattern of occurrences of vaping or bullying.In case a decrease or increase pattern is identified, the control server120 may adjust the base data for identifying bullying to make thedetection sensor 110 more or less sensitive to the identification. Inthis way, the base data may be adjusted based on the trend of thehistory data.

For example, FIGS. 3B and 3C show history data of detected sound leveland detected air quality, respectively. The horizontal axes for bothgraphs of the history data represent time, the vertical axis of FIG. 3Brepresents decibel or voltage amplitude, and the vertical axis of FIG.3C represents air quality index. The history data of the detected soundsobtained during the learning mode is used to generate base data foridentifying bullying or sleep apnea at the installation location in theactive mode. As the detected sound fluctuates, the threshold value foridentification may vary according to the times. For example, thethreshold value for detecting bullying at dawn may be lower than thethreshold value for detecting bullying at noon. It may also vary basedon the day of week and location. The threshold value on Wednesday may behigher than on Sunday at a school. On the other hand, the thresholdvalue on Wednesday may be lower than on Sunday at a commercialestablishment such as a department store.

In an aspect, the detection sensors 110 may repeat the learning mode andactive mode consecutively. As shown in FIG. 3C, the first period (e.g.,about ten seconds from the start to 09:31:38) may be used in thelearning mode to collect data regarding the environment. Then, thedetection sensor 110 determines whether an adjustment or calibrationneeds to be made to the modified hydrogen sensor so as to properlydetect vaping. For example, the voltage or resistance in the modifiedhydrogen sensor varies depending on temperature of the environment.Thus, the modified hydrogen sensor can be adjusted or calibrated basedon the environment.

After the first period for collecting environment-calibrated data, thethreshold value for vaping is determined in the active mode for a secondperiod and the detection sensor 110 detects vaping based on thethreshold value.

In another aspect, the detection sensors 110 may iterate the learningmode and the active mode after the first and second periods, meaningthat the detection sensors 110 may calibrate the modified hydrogensensor repeatedly so that the detection sensor 110 may accurately detectvaping.

FIG. 3C shows two curves. The upper curve represents threshold indexvalue for identifying vaping. The lower curve represents the historydata of detection results from the air quality sensor of the detectionsensor 110. The upper curve is stabilized in a period of time after thepower-up.

In an aspect, the detection sensors 110 may repeat the learning mode andactive mode consecutively. As shown in FIG. 3C, the first period (e.g.,about ten seconds from the start to 09:31:38) may be used in thelearning mode to collect data regarding the environment. Then, thedetection sensor 110 determines whether an adjustment or calibrationneeds to be made to the modified hydrogen sensor so as to properlydetect vaping. For example, the voltage or resistance in the modifiedhydrogen sensor varies depending on temperature of the environment.Thus, the modified hydrogen sensor can be adjusted or calibrated basedon the environment.

After the first period for collecting environment-calibrated data, thethreshold value for vaping is determined in the active mode for a secondperiod and the detection sensor 110 detects vaping based on thethreshold value.

In another aspect, the detection sensors 110 may iterate the learningmode and the active mode after the first and second periods, meaningthat the detection sensors 110 may calibrate the modified hydrogensensor repeatedly so that the detection sensor 110 may accurately detectvaping based on the index value.

The index value is calculated based on the temperature, moisture, andthe detection results of the modified hydrogen sensor. For example, thetemperature falls in a range between 60 and 80 degree Fahrenheit, themoisture is increased by at least 10 percent, and the hydrogen increasesfrom the base level (e.g., environment level) by approximately 10percent, the detection sensor 110 may determine that vaping hasoccurred. This determination is provided as an example and is notprovided to limit the scope of this application.

In an aspect, the control server 120 may send a command to the detectionsensor 110 to adjust internal parameters for detecting bullying andvaping based on the trend identified from the history data. Further, thecontrol server 120 may communicate with the detection sensors 110 bycalling functions of application programming interface (“API”) betweenthe detection sensor 110 and the control server 120. In this regard, thedetection sensor 110 can push detection results to the control server120 and respond to the control server 120's request.

In an aspect, the control server 120 may not store detected results fromthe detection sensors 110 because of privacy issues. Nevertheless, thecontrol sever 120 may provide signals back to the detection sensors 110to indicate tuning parameters and false positives.

Internal parameters of the detection sensor 110 may include LEDfunctionality, sound threshold, networking server IP address, alerttimeout, serial number, reboot for device required or not, latest binarycode, vape identification algorithm parameters. This list of parametersshould not be understood as exhaustive but provided only for examplepurposes. The internal parameters of the detection sensor 110 mayfurther include bullying identification algorithm parameters. Bullyingor vaping identification algorithm parameters may include a window sizeor threshold values or ranges.

In an aspect, the control server 120 may update internal parameters viatext or binary files. Internal parameters for each the detection sensor110 may be saved in the database 130.

In another aspect, the control server 120 may control the detectionsensors 110 collectively, individually, or group by group. For example,several the detection sensors 110 may be installed at the same site.When they need to update internal parameters or settings, the controlserver 120 may control the detection sensors 110 collectively at thesite. However, such control may not affect the detection sensor 110installed in the other sites. The control server 120 may use a querylanguage to request data from the database 130. The query language maybe SQL, MySQL, SSP, C, C++, C#, PHP, SAP, Sybase, Java, JavaScript, orany language, which can be used to request data from a database.

In yet another aspect, even when several detection sensors 110 areinstalled at the same site, the control server 120 may control themdifferently because one the detection sensor 110 may have differentparameters for identifying bullying and vaping from those of another thedetection sensor 110 due to different installation locations at thesite. For example, the detection sensor 110 installed at a bedroom hasparameters different from those of the detection sensor 110 installed ata bathroom.

Clients 170 may log in to the control server 120 to see graphicalrepresentations of the detection results from the detection sensor 110via Internet. Communication between the clients 170 and the controlserver 120 may utilize http, https, ftp, SMTP, or related Internetprotocols. The clients 170 may be able to adjust settings for each thedetection sensor 110. For example, the settings may include a mode ofwarnings (e.g., an email, text message, telephone call, instant message,audible warning, etc.), an address, to which such warnings are to besent in case of identification of bullying or vaping, and the like. Theclients 170 are the ones who are responsible for the sites where thedetection sensors 110 are installed for identifying bullying and vaping.For example, the clients 170 may be a principal, vice president, orperson in charge at a school, a president at a company, a manager at ahospital or any commercial establishment, or security personnel. Thislist, however, is not meant to be exhaustive but is provided only forshowing examples. Other peoples in different rankings, at differentlocations can be included in this list.

When the detection sensor 110 identifies bullying or vaping, thedetection sensor 110 may send an alert to the clients 170 via a clientserver 160 using protocols of Internet. The client server 160 may beused for sending a simple message or email to the clients 170supervising the site, where the bullying or vaping is detected. Theclient server 160 may manage the clients 160 registered on the clientserver 160 and show alert history and other notification upon requestsfrom the clients 160. Further, the client server 160 may handlecustomizing or fine tuning the detection sensors 110, which lead to analert when the detection sensors 110 need to reboot, update, or receiveconfiguration. In an aspect, as dotted lines are shown in FIG. 1, thecommunication between the client server 160 and the clients 170 may notbe regularly performed but can be made only when bullying or vaping isidentified. The clients 170 may receive the alert on a computer, smartdevice, or mobile phone. In this way, the clients 170 are not swamped byoverwhelming number of messages because they receive the alert only whenbullying or vaping is identified. Further, the clients 170 may be ableto timely, properly supervise at the site whenever an alert is received.

When the client server 160 receives an alert from the detection sensor110, the client server 160 may communicate with the message server 140,which manages pushing alerts to the notification subscribers 150. Theclients 170 may be the persons in charge as the first contact person whohas a direct access to the control server 120 for the site, and thenotification subscribers 150 may be any related personnel as the secondcontact persons who do not have a direct access to the control server120. Similar to the ways the client server 160 sends alerts to theclients 170, the message server 140 sends alerts to the notificationsubscribers 150 via a text message, email, instant message, telephonecall, audible warning, any communication means readily available to aperson having skill in the art. The notification subscribers 150 mayreceive alerts via a computer, smart device, mobile phone, personaldigital assistant, tablet, or any available means for receiving suchalerts.

As described above, vaping can be identified when the signature isdetected, meaning that vaping can be identified independent of locationsand times. Thus, features related to identification of vaping may beintegrated into the detection sensor 110. In this case, when vaping isidentified, the detection sensor 110 may bypass the control server 120and directly communicate with the message server 140 and the clientserver 160 to transmit alerts to ones in charge or responsible for thesites where the detection sensor 110 are installed. On the other hand,identification of bullying is different from site to site due todifferent environments. In other words, when sounds are detected by thedetection sensor 110, the control server 120 receives and analyzes thedetected sounds, and determines whether bullying has occurred. As aresult, vaping may be identified earlier than bullying, and alerts forvaping may be sent to the notification subscribers 150 and the clients170 faster than alerts for bullying.

In an aspect, features for identifying bullying may be also integratedinto the detection sensor 110. This can be done by the control server120 controlling the detection sensor 110 to update internal parametersfor identifying bullying at the corresponding site. In this case, thecontrol server 120 regularly checks the history data stored at thedatabase 130 and regularly update the internal parameters of thedetection sensor 110 for identifying bullying. After updating theinternal parameters of the detection sensor 110, alerts for identifyingbullying may be sent to the notification subscribers 150 and the clients170 in the same way as alerts for identifying vaping are sent.

Now referring back to FIG. 2, a functional block diagram of thedetection sensor 110 of FIG. 1 is shown in accordance with embodimentsof the present disclosure. The detection sensor 110 may include a soundsensor 210, an air quality sensor 220, a network interface 230, a powerunit 240, and a controller 250. The sound sensor 210 may be used fordetecting sound and the air quality sensor 220 may be used for detectingair quality.

In particular, the sound sensor 210 detects sound levels (e.g., decibel(dB)) in the environment. For example, FIG. 3A shows detected soundlevels in the form of voltage amplitudes. The horizontal axis representstime and the vertical axis represents voltage amplitude. Curvesrepresent detected sound levels in voltage. The bold lines representwindows for identification. For example, the window of identificationmay be less than 1 second. Within the window, when the voltage amplitudeis greater than a threshold value, bullying may be identified. In thisexample, the threshold value is about 4.9 volts. Thus, between 4 and 5seconds, bullying may be identified.

As described above, the threshold value for identifying bullying dependson the installation location at the site and based on history dataobtained during the learning mode. Since the detection sensor 110 maycover a limited area, several satellite detection sensors 110 may beinstalled at one enclosed space when the area of the enclosed space isgreater than the area each satellite detection sensor 110 can cover. Forexample, the detection sensor 110 may cover an area of 10 by 10 squarefeet. In this situation, each satellite detection sensor 110 may havedifferent threshold value for identifying bullying due to differentinstallation locations at the same enclosed space. The air qualitysensor 220 may detect air quality including moisture and hydrogencontent in the air and temperature of the air. In other words, the airquality sensor 220 may include a combination of sensors sensing airquality. In an aspect, the air quality sensor 220 may include othersensors sensing air content of the environment. Vaping may be detectedby specific range combination of humidity, hydrogen, and temperature,which is defined as signature in this disclosure. Since the signaturedoes not depend on installation locations and times, internal parametersfor identifying vaping may be predetermined. In other words, the airquality sensor 220 does not need training, while the sound sensor 210needs training. The network interface 230 may be configured to transmitsensed results to the control server 120. In an aspect, the networkinterface 230 may transmit a request to send an alert, when bullying orvaping is identified, to the message server 140 and the client server160. Further, the network interface 230 may receive a command to updateinternal settings or parameters from the control server 120.

In an aspect, the network interface 230 may communicate with otherswirelessly or via a wired connection. Wireless connections may be widearea network (WAN), local area network (LAN), personal area network(PAN), ad hoc network, cellular network, etc. Wired network may utilizecategory 5 cable (CAT5), CAT5E, category 6 cable (CAT6), or similarcables. The sound sensor 210, the air quality sensor 220, and thenetwork interface 230 may be powered by the power unit 240. Regularbatteries may be installed to supply power to the detection sensor 110.For example, AA, AAA, or other suitable batteries may be used. The powerunit 240 may utilize batteries and a connection to a power outlet sothat the power unit 240 may supply power by using the batteries just incase when the power is out.

In an aspect, the power unit 240 may receive power supplied from anetwork cable, such as CAT5 or CAT6, which is called power-over-Ethernet(PoE) or active Ethernet. PoE+ and 4PPoE may be also used to supplypower. Since the network cable supplies power, the detection sensor 110may be installed everywhere the network cable can be installed withoutworrying about a distance to a power outlet. Also, since the power unit240 does not need electric components necessary for connections to apower outlet, manufacturing cost can be lowered and the size of thedetection sensor 110 can be reduced. The detection sensor 110 furtherincludes the controller 250, which controls functions and settings ofthe detection sensor 110. When the detection sensor 110 is powered, thecontroller 250 sets settings of the detection sensor 110 and internalparameters of the sound sensor 210 and the air quality sensor 220. Thecontroller 250 further controls the network interface 230 to transmitdetected results or requests for sending alerts when bullying, sleepapnea, or vaping is detected, and reset or update settings and internalparameters upon reception of update command from the control server 120.

The controller 250 may be implemented on Linux, Windows, android, IOS,or similar software operation system. In an aspect, the controller 250may be implemented on a hardware system, such as a digital signalprocessor (DSP), application-specific integrated circuit (ASIC),field-programmable gate array (FPGA), different types of programmableread-only memory (e.g., PROM, EPROM, EEPROM, etc.), or microprocessorsuch as Raspberry Pi.

In an aspect, the controller 250 may be implemented on a hardware systemby removing unnecessary features from the hardware system to reducepower consumption and integrating necessary features for identificationinto the hardware system. For example, the controller 250 may beimplemented on a Raspberry Pi by removing unnecessary features, whichwere already equipped in the Raspberry Pi, and by integrating featuresfor identifying vaping. In this way, power required for running thesound sensor 210, the air quality sensor 220, the network interface 230,and the controller 250 can be sufficiently supplied via a network cable.This approach for reducing power consumption may be applied to otherhardware systems or software operating systems.

In an aspect, the detection sensor 110 may not be equipped with awarning system. Thus, when bullying or vaping is detected at theinstallation site, any person who bullies or vapes cannot recognize thatthe identification of such is reported to the clients 170 and thenotification subscribers 150 because the identification is reportedsilently to the person.

FIG. 4 shows a flowchart for a method 400 in the learning mode inaccordance with embodiments of the present disclosure. As describedabove, the sound sensor 210 of the detection sensor 110 needs trainingto generate base data. In the learning mode, the base data is generated.In step 410, the sound sensor detects sounds for a predetermined period.The detected sound is combined with the corresponding timestamp in step420. The timestamp may include the time, the day of the week, the day,and the month when the sound is detected. The combined data is thensaved in a database in step 430.

In step 440, it is checked whether or not the learning mode is stilltrue. If it is true, the method 400 repeats steps 410-440 untilsufficient sound data is saved in the database. In an aspect, the sounddata may be saved in a memory in the detection sensor 110 but not in thedata base, which is distant from the detection sensor 110, forprotecting privacy.

If it is determined that the learning mode is false in step 440, themethod 400 proceeds to step 450, in which base data is generated basedon the detected sounds saved at the database during the learning mode.The base data may include a series of threshold values for identifyingbullying or sleep apnea along the time of each day, each week, or eachmonth depending on the total duration of the learning mode. Aftergeneration of the base data, the method 400 ends.

Now turning to FIG. 5, a method 500 is provided in the active mode inaccordance with embodiments of the present disclosure. After the basedata is generated in method 400 of FIG. 4, the method 500 starts withsteps 510 and 560. In step 510, the sound sensor detects sound in theenvironment and in step 560, the air quality sensor detects air quality.In the method 500, detections of sound and air quality are shownparallelly. In an aspect, such detections may be serially performed.

In step 520, timestamp is provided to the detected sounds. Based on thetimestamp, a control system makes a request for history data from thedatabase in step 530. The control system then determines based on thehistory data whether or not noise disturbance is detected in step 540.The noise disturbance may be related to bullying or sleep apnea. In anaspect, the noise disturbance may be related to sound related phenomenaor situations, such as fights, hurricane, voice recognition, etc.

If it is determined that the noise disturbance is identified in step540, the control system silently sends an alert to one or more users whoare in charge of the installation site in step 550. After sending thealert, the method 500 restarts the process.

If it is determined that the noise disturbance is not identified in step540, steps 510-550 are repeated.

Now returning back to the air quality detection, after the air qualityis detected in step 560, the control system determines whether or notthe signature is identified in step 570. If it is determined that thesignature is identified in step 570, the control system silently sendsan alert to the one or more users via a text message, email, instantmessage, optical warning, or oral warning in step 550.

In case when it is determined that the signature is not identified instep 570, the method 500 repeats steps 560 and 570. In this way, sleepapnea, bullying, or vaping can be detected and informed to the users.Peoples at the site, however, may not acknowledge the transmission ofthe alert because the alert is transmitted silently to the people at thesite.

Turning now to FIG. 6, a flowchart is provided for a method 600 fordetecting vape. The method starts from sensing temperature and humidityin step 610. As described above, the modified hydrogen sensor of thedetection sensor may vary because the voltage or resistance in themodified hydrogen sensor varies depending on temperature of theenvironment. Thus, in step 620, it is determined whether an adjustmentto the modified hydrogen sensor is needed.

When it is determined that the adjustment is needed in step 620, thevoltage or resistance of the modified hydrogen sensor is adjusted toappropriately sense gas (e.g., hydrogen) in step 630 and then the method600 moves to step 640.

When it is determined that the adjustment is not needed in step 620, themodified gas sensor reads gas in step 640.

In step 650, it is determined whether the sensed temperature, humidity,and gas match abnormality matching signature, meaning that the sensedresults are within the corresponding ranges. When they match theabnormality matching signature, an alert is sent in step 660. Otherwise,the method 600 goes back to step 610 and repeats steps 610-660.

Turning now to FIG. 7, a simplified block diagram is provided for acomputing device 700, which can be implemented as the control server120, the database 130, the message server 140, and the client server 160of FIG. 1. The computing device 700 may include a memory 702, aprocessor 704, a display 706, a network interface 708, an input device710, and/or an output module 712. The memory 702 includes anynon-transitory computer-readable storage media for storing data and/orsoftware that is executable by the processor 704 and which controls theoperation of the computing device 700.

In an aspect, the memory 702 may include one or more solid-state storagedevices such as flash memory chips. Alternatively or in addition to theone or more solid-state storage devices, the memory 702 may include oneor more mass storage devices connected to the processor 704 through amass storage controller (not shown) and a communications bus (notshown). Although the description of computer-readable media containedherein refers to a solid-state storage, it should be appreciated bythose skilled in the art that computer-readable storage media can be anyavailable media that can be accessed by the processor 704. That is,computer readable storage media may include non-transitory, volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. For example, computer-readable storage media includes RAM,ROM, EPROM, EEPROM, flash memory or other solid state memory technology,CD-ROM, DVD, Blu-Ray or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by the computing device 700.

The memory 702 may store application 716 and/or data 714 (e.g., basedata and history data from the sound sensor 210 and the air qualitysensor 220 of FIG. 2). The application 716 may, when executed byprocessor 704, cause the display 706 to present the user interface 718including FIGS. 3A-3C. The processor 704 may be a general purposeprocessor, a specialized graphics processing unit (GPU) configured toperform specific graphics processing tasks while freeing up the generalpurpose processor to perform other tasks, and/or any number orcombination of such processors. The display 706 may be touch-sensitiveand/or voice-activated, enabling the display 706 to serve as both aninput and output device. Alternatively, a keyboard (not shown), mouse(not shown), or other data input devices may be employed. The networkinterface 708 may be configured to connect to a network such as a localarea network (LAN) consisting of a wired network and/or a wirelessnetwork, a wide area network (WAN), a wireless mobile network, aBluetooth network, and/or the internet.

For example, the computing device 700 may receive, through the networkinterface 708, detection results for the detection sensor 110 of FIG. 1,for example, detected sound in the learning mode and the active mode,and history data, which is time-series data including detected soundsand detected air quality from the detection sensor 110 for the wholerunning times or a predetermined period. The computing device 700 mayreceive updates to its software, for example, the application 716, viathe network interface 708. The computing device 700 may also displaynotifications on the display 706 that a software update is available.

The input device 710 may be any device by means of which a user mayinteract with the computing device 700, such as, for example, a mouse,keyboard, foot pedal, touch screen, and/or voice interface. The outputmodule 712 may include any connectivity port or bus, such as, forexample, parallel ports, serial ports, universal serial busses (USB), orany other similar connectivity port known to those skilled in the art.The application 716 may be one or more software programs stored in thememory 702 and executed by the processor 704 of the computing device700. The application 716 may be installed directly on the computingdevice 700 or via the network interface 708. The application 716 may runnatively on the computing device 700, as a web-based application, or anyother format known to those skilled in the art.

In an aspect, the application 716 will be a single software programhaving all of the features and functionality described in the presentdisclosure. In other aspect, the application 716 may be two or moredistinct software programs providing various parts of these features andfunctionality. Various software programs forming part of the application716 may be enabled to communicate with each other and/or import andexport various settings and parameters relating to the identification ofbullying, sleep apnea, and vaping. The application 716 communicates witha user interface 718 which generates a user interface for presentingvisual interactive features to the notification subscribers 150 or theclients 170 of FIG. 1 on the display 706. For example, the userinterface 718 may generate a graphical user interface (GUI) and outputthe GUI to the display 706 to present graphical illustrations such asFIGS. 3A-3C.

Since other modifications and changes may be made to fit particularoperating requirements and environments, it is to be understood by oneskilled in the art that the present disclosure is not limited to theexamples described in the present disclosure and may cover various otherchanges and modifications which do not depart from the spirit or scopeof this disclosure.

What is claimed is:
 1. A sensor system comprising: an air quality sensorconfigured to detect air quality, the air quality sensor including acombination of sensors configured to sense air quality; and a networkinterface configured to transmit a signal indicating abnormalitymatching signature of vaping, and a controller configured to identifyvaping based on the detected air quality, wherein the vaping isidentified when the detected air quality includes the abnormalitymatching signature.
 2. The sensor system according to claim 1, whereinthe abnormality matching signature includes a temperature range, ahydrogen range, and a humidity range.
 3. The sensor system according toclaim 2, wherein the sensor system is powered via power over Ethernet orpower over Ethernet+, or powered by a power outlet, wherein the power issupplied by a CAT5, CAT5E, or CAT6 cable.
 4. The sensor system accordingto claim 3, wherein the signal indicating abnormality matching signatureof vaping is transmitted via the CAT5, CAT5E, or CAT6 cable, orwirelessly via WiFi or cellular.
 5. The sensor system according to claim1, wherein the air quality sensor is location-independent.
 6. The sensorsystem according to claim 1, further comprising a sound detectorconfigured to detect sounds, wherein the controller is configured toidentify potential bullying based on the detected sounds, wherein thenetwork interface is configured to transmit a signal indicatingpotential bullying, and wherein the sound detector islocation-dependent.
 7. The sensor system according to claim 6, whereinthe sound detector is configured to detect training sounds in apredetermined period in a learning mode prior to identification of thepotential bullying.
 8. The sensor system according to claim 7, whereinthe detected training sounds during the predetermined period generatebase data for identifying the potential bullying.
 9. The sensor systemaccording to claim 8, wherein the potential bullying is identified whenthe detected sounds are greater than or equal to a threshold value basedon the base data.
 10. The sensor system according to claim 1, wherein analert is transmitted to a user when the vaping is identified.
 11. Thesensor system according to claim 10, wherein the alert is a textmessage, an email, an optical flashing, an audible sound, or combinationthereof.
 12. The sensor system according to claim 1, wherein the sensorsystem is run by a mobile operating system.
 13. An identification systemcomprising: a sensor system comprising: an air quality sensor configuredto detect air quality, the air quality sensor including a combination ofsensors configured to sense air quality, and a network interfaceconfigured to transmit the sensed air quality; and a controller coupledto the sensor system via a network and configured to identify vapingbased on the sensed air quality and to send an alert to a user, whereinthe vaping is identified when the sensed air quality includes anabnormality matching signature of vaping.
 14. The identification systemaccording to claim 13, wherein the abnormality matching signatureincludes a temperature range, a hydrogen range, and a humidity range.15. The identification system according to claim 14, wherein the sensorsystem is powered via power over Ethernet or power over Ethernet+,wherein the power is supplied by a CAT5, CAT5E, or CAT6 cable.
 16. Theidentification system according to claim 15, wherein the sensed airquality is transmitted via the CAT5, CAT5E, or CAT6 cable, or wirelesswirelessly via WiFi or cellular.
 17. The identification system accordingto claim 13, wherein the air quality sensor is location independent. 18.The identification system according to claim 13, wherein the sensorsystem further comprises a sound detector configured to detect sounds,wherein the network interface is configured to transmit the detectedsounds to the controller via the network, wherein the controller isconfigured to identify potential bullying based on the detected sounds,and wherein the sound detector is location dependent.
 19. Theidentification system according to claim 18, wherein the sound detectoris configured to detect training sounds in a predetermined period in alearning mode prior to identification of the potential bullying.
 20. Theidentification system according to claim 19, wherein the detectedtraining sounds during the predetermined period generate base data fordetecting the potential bullying.
 21. The identification systemaccording to claim 20, wherein the potential bullying is identified whenthe detected sounds are greater than or equal to a threshold value basedon the base data.
 22. The identification system according to claim 13,wherein the alert is transmitted silently to a user when the vaping isidentified.
 23. The identification system according to claim 22, whereinthe alert is a text message, an email, an optical flashing, an audiblesound, or combination thereof.
 24. The identification system accordingto claim 13, wherein the sensor system is run by a mobile operatingsystem.
 25. A sensor system for identifying vaping at a site, the sensorsystem comprising: an air quality sensor configured to detect airquality; and a network interface configured to transmit a signalindicating abnormality matching signature of vaping, wherein the vapingis identified when the detected air quality includes a signature. 26.The sensor system according to claim 25, wherein the signature includesa temperature range, a hydrogen range, and a humidity range.
 27. Thesensor system according to claim 25, further comprising a controllercoupled to a network and configured to send an alert to a user at a timeafter the vaping is identified.
 28. The sensor system according to claim27, wherein the alert is transmitted silently to the user when thevaping is identified.
 29. The sensor system according to claim 27,wherein the alert is a text message, an email, an optical flashing, anaudible sound, or combination thereof.
 30. The sensor system accordingto claim 25, wherein the sensor system is powered via power supplied bya CAT5, CAT5E, or CAT6 cable.